DNA Microarrays in Lymphoid Malignancies

DNA Microarrays in Lymphoid Malignancies

In the introduction to his book,
The Order of Things, the French
structuralist philosopher Michel
Foucault posed the question: "When
we establish a considered classification,
when we say that a cat and a
dog resemble each other less than
two greyhounds do, on what grounds
are we able to establish the validity
of this classification with complete
certainty?" He went on: "On what
table, according to what grid of identities,
similarities, analogies do we
sort out so many different and similar
things."
Individualized Diagnosis
As Rosenwald summarizes in his
article, the grid that has been recently
used with success in studying lymphoma
types and subtypes and holds
promise for future clinical application
is the DNA microarray. The reason
that DNA microarrays-by virtue of
analyzing the expression levels of
thousands of genes-are such powerful
tools is simply the following basic
notion (as expressed by the 18th century
French naturalist Buffon): "The
more we increase the number of divisions
in the production of nature, the
closer we shall approach to the truth,
since nothing really exists in nature
except the individual." In his article,
Rosenwald illustrates how lymphoma
diagnosis can be "individualized" and
what the clinical potential of doing so
could be. But in the words of Foucault,
"we have to critically evaluate the
grounds on which we are able to establish
the validity of a more individualized
lymphoma classification."
Prior to the 1970s, the diagnosis
and classification of lymphoma was
based purely on histologic examination.[
1] Later, with the advent of
monoclonal antibodies specific for a
multitude of lymphoid antigens and
the application of these antibodies in
immunophenotyping, lymphoma diagnosis
could be more refined. During
the early days of molecular diagnostics
of lymphoid malignancies, the
ability to establish a diagnosis of neoplastic
disease by demonstrating the
clonal nature of the lymphoid population
represented another quantum
leap. In subsequent years, a vast
amount of information has accumulated
on the molecular pathology of
various lymphoid malignancies.
These developments ultimately led to
the recognition of biological entities,
reflected in the Revised European-
American Classification of Lymphoid
Neoplasms[2] and updated in the recent
World Health Organization lymphoma
classification.[3]
Further biologic variables of these
established lymphoma entities have
already been recognized, but as discussed
by Rosenwald, microarray
analysis can help to identify these
variables more exhaustively, objectively,
and reproducibly. This brings
us one step closer to an "individualized"
diagnosis. In addition, and
importantly, aberrant molecular
pathways can be revealed in lymphomas,
opening the door to more
specific treatment. Indeed, establishing
a direct link between precise diagnosis
and treatment should be the
ultimate goal.
Gene Expression Profiling
DNA microarray technology is
possible largely due to the structural
genomic foundation established by
several large-scale human genome
projects. It allows the study of genome-
wide gene expression profiles in
physiologic and disease processes. The
technology has been employed to
study a wide variety of human tumors
with the expectation that it will engender
a better understanding of the molecular
mechanisms underlying the
behavior of a tumor. Various microarray
platforms have been used for
studying gene expression profiles. The
studies conducted by Alizadeh,
Rosenwald, and coworkers employed
a cDNA microarray, the Lymphochip.
Major concerns about the use of this
technology include considerations of
how readily we can compare studies
across different platforms and how such
comparisons can be facilitated. In this
regard, it is important that sufficient information
on experimental design and
data processing be available so that the
experimental data can be independently
analyzed by other investigators. A crossplatform
comparison of data is a challenging
endeavor, and one example is
the recently published comparison of
two sets of microarray data on diffuse
large B-cell lymphoma (DLBCL)-one
based on the Affymetrix platform and
the other, on the Lymphochip platform.[
4] It is interesting that the germinal
center B-cell and activated
B-cell subsets of DLBCL could be defined
based on Affymetrix data, and
furthermore, that the subsets had different
overall survival rates.
Molecular Prognosticators
While the molecular prognosticators
defined by the studies of Rosenwald
et al can be used in addition to or alternatively
to the International Prognostic
Index and have been validated
in the studies by these investigators,
several unresolved issues remain. The
number of array elements on the
Lymphochip, although substantial,
represents only a fraction of the
transcriptome. Will the predictors
change substantially if the study is
repeated using an array with a more
complete representation of the
transcriptome?
As mentioned above, a molecular
prognosticator will be of value only
as long as it reflects the variables of
the biologic response to specific treatments.
The molecular prognosticator
for DLBCL established by Rosenwald
et al was based on a series of patients
treated with anthracycline-based
multiagent chemotherapy.[5] Currently,
patients with DLBCL are often
treated with regimens containing
rituximab (Rituxan), which may significantly
alter their response and survival.
Will the molecular prognosticator
based on samples from patients
treated with anthracycline-based chemotherapy
be valid for patients treated
with current modalities?
Although gene expression profiling
is a powerful technique, additional information
is useful in supervising the
discovery process. Clinical information
was used to supervise the discovery
of prognosticators, and immunoglobulin
variable region gene mutation
information was essential to finding
differentially expressed genes in mutated
and nonmutated chronic lymphocytic
leukemia cases.[6,7] It is likely
that certain unique genetic abnormalities
within a tumor subset may also be
used to guide similar discovery processes.
The integration of global genetic
and gene expression data will
certainly be highly synergistic in future
investigations.
Conclusions
Nonetheless, Rosenwald's conclusion
is well founded. Gene expression
profiling will have a dramatic impact
on the diagnosis of lymphoma, adding
many variables to the grid by
which diagnoses are made. In the foreseeable
future, the initial characterization
of a lymphoma case may be
performed on a single platform, which
could be either a diagnostic DNAminichip,
an antibody panel, or a realtime
reverse transciptase-polymerase
chain reaction plate, and, no doubt,
these instruments will be refined further.
The ultimate goal is to treat patients
with tailored therapy, based on
targeting aberrant biologic pathways
detected in the individual tumor.

Disclosures

The author(s) have no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.